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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Validation of Classification Model

s
smruti Last Updated : 11 Jan, 2021
6 min read

This article was published as a part of the Data Science Blogathon.

Introduction

The main objectives of a model validation include the testing of the model’s conceptual soundness and continued fit for purpose, including identification of potential risks and limitations. These tests must constitute an effective challenge to the existing production model for the benefit of its improvement, risk mitigation. The data for this exercise is taken from here.

Problem Statement

Validate the logistic regression model developed to predict the likelihood an applicant of applying for the loan. The lending companies work by analyzing the financial history of their loan applicants.

 

Validation Framework

The following tests were carried out to validate the model results:

  1. Data checks – Dependent and Independent (Missing and Outlier)
  2. Model variables – IV, Linearity, and VIF
  3. 3. Model fit – AUROC, Gini, KS, and Gain & Lift Charts
  4. 4. Model tests – Sensitivity Analysis 
  5. 5. Coefficient Stability – Sign stability and Coefficient stability

2.1 Data Checks

Dependent Variables

The full data is divided into train data (60% of full data) and test data (40% of full data). The model is developed on the train data and validated on test data.

dependent variable

It is observed that the distribution of response (Y=1) and no response (Y=0) is very similar among full data, train data, and test data.

· Response (Y=1) indicates the applicant has applied for the loan

· No response (Y=0) indicates the applicant has not applied for the loan

responses

 

Independent Variables

Independent variables include personal and financial information. Risk scores are leveraged to develop the model. There are 5 independent variables in the model.

It is observed that there are no missing values in the train dataset.

independent variables validation

It is observed that there are outliers in the train dataset. Inter Quartile Range (IQR = Q3 – Q1) is used to identify the outlets. Outliers are values more than the upper bound (Q1 + 1.5 x IQR) or values less than the lower bound (Q1 – 1.5 x IQR). The validation team recommends that the outliers should be treated before developing the model.

validation team

2.2. Model Variables

Information Value (IV)

from statsmodels.stats.outliers_influence import variance_inflation_factor

The explanatory power of the variable is captured using IV. As the explanatory power of the variable increases, the IV increases. It is observed that all the variables have IV < 0.1, indicating have low explanatory power in the train dataset.

information value validation

Linearity

Step 1 – make 10 bins for each numerical variable
Step 2 – for each bin calculate the mean of the variable and corresponding log-odds 

Linearity is checked for the numerical variables (age, amount_requested, risk_score, and ext_quality_score) in the train data. It is observed that age & risk_score are linear and amount_requested & ext_quality_score are non-linear. The validation team recommends that the transformations should be tested for getting a linear relationship.

validation linearity

Variance Information Factor (VIF)

VIF indicates the multicollinearity between the independent variables. It is observed that their VIF is less than 2 in the training dataset. VIF less than 2 indicates no multicollinearity. Home_owner is a flag, hence not considered for VIF.

2.3 Model Fit

AU-ROC

 sklearn.metrics.auc(x, y)

The area under Receiver Operator Curve (AUROC) is used to measure the predictive power of the model. AUROC = 0.50 indicates no predictive power and AUROC = 1.00 indicates perfect predictive power. The model developed on train data is run on test data and full data. It is observed that there is not a significant deviation in the AUROC values.

It is observed that AUROC is less than 0.6. This indicates that the model does not have good predictive power. The validation team recommends using additional variables to improve the model fit.

validation AU roc

Gini

Formula: Gini = 2 x AUROC – 1

Gini is derived from AUROC. Gini = 0.0 indicates no predictive power and Gini = 1.0 indicates perfect predictive power. The model developed on train data is run on test data and full data. It is observed that there is not a significant deviation in the Gini values.

validation gini

KS

scipy.stats.ks_2samp

Kolmogorov-Smirnov (KS) test measures the separation between cumulative % event and cumulative % non-event. It is observed that KS test statistics are less than 40, indicating that the model is not able to separate events and non-events.

validation ks

Gain & Lift Charts

Step 1: Calculate probability for each observation
Step 2: Rank these probabilities in decreasing order.
Step 3: Build deciles with each group having almost 10% of the observations.
Step 4: Calculate the response rate at each decile for Good (Responders), Bad (Non-responders), and total.

Gain & lift charts are data visualization tools that compare the ability of the classifier to capture the response rate. It is observed that the cumulative predicted response rate is very close to the cumulative random response rate. It indicates that the model has low predictive power. The validation team recommends using additional variables to improve the model fit.

gain and lift chart validation

2.4 Model tests

Sensitivity Analysis

Step 1 – normalize all the variables
Step 2 – run logistic regression between the dependent and the first variable
Step 3 – run logistic regression between the dependent and the second variable
Step 4 – repeat the above step for rest of the variables
Step 5 – the coefficient of the variable indicates the sensitivity between the variable and the log-odds of the dependent variable 

The sensitivity of the model with respect to the independent numerical variables is tested. The sensitivity is checked on the train data. The objective of this exercise is to identify the most sensitive variables. It is observed that age and risk_score are the most sensitive variables and ext_quality_score is the least sensitive variable.

sensitivity

 

2.5. Coefficient Stability

Coefficient stability

The model is re-estimated on test data & full data and the coefficients are compared with the train data. If the coefficients of the re-estimated model are within the 95% confidence interval (Train coef ± 1.96 x Train std err) then the coefficients are stable.

The lower bound is defined as Train coef – 1.96 x Train std err and the upper bound is defined as Train coef + 1.96 x Train std err. It is observed that the coefficients are stable.

Coefficient stability

 

Sign stability

The model is re-estimated on test data & full data and the coefficients are compared with the train data.

It is observed that the signs are stable.

Train and test data

 

Conclusion

The validation found the model to be stable. However, three severity findings have been raised:

· Finding 1 (input data) – It is observed that there are outliers in the train dataset. The validation team recommends that the outliers should be treated before developing the model.

· Finding 2 (input data) – It is observed that amount_requested & ext_quality_score are non-linear. The validation team recommends that the transformations should be tested for getting a linear relationship.

· Finding 3 (model fit) – It is observed that AUROC is low, Gini is low and KS is low indicating that the model is not able to separate events and non-events. The validation team recommends using additional variables to improve the model fit.

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion.

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